Skip Navigation


Bioinformatics Advance Access originally published online on June 16, 2005
Bioinformatics 2005 21(18):3604-3609; doi:10.1093/bioinformatics/bti542
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow All Versions of this Article:
21/18/3604    most recent
bti542v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (8)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Petryszak, R.
Right arrow Articles by Apweiler, R.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Petryszak, R.
Right arrow Articles by Apweiler, R.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

The predictive power of the CluSTr database

Robert Petryszak , Ernst Kretschmann , Daniela Wieser and Rolf Apweiler *

EMBL Outstation Hinxton, The European Bioinformatics Institute (EBI) Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK

*To whom correspondence should be addressed.

Summary: The CluSTr database employs a fully automatic single-linkage hierarchical clustering method based on a similarity matrix. In order to compute the matrix, first all-against-all pair-wise comparisons between protein sequences are computed using the Smith–Waterman algorithm. The statistical significance of the similarity scores is then assessed using a Monte Carlo analysis, yielding Z-values, which are used to populate the matrix. This paper describes automated annotation experiments that quantify the predictive power and hence the biological relevance of the CluSTr data. The experiments utilized the UniProt data-mining framework to derive annotation predictions using combinations of InterPro and CluSTr. We show that this combination of data sources greatly increases the precision of predictions made by the data-mining framework, compared with the use of InterPro data alone. We conclude that the CluSTr approach to clustering proteins makes a valuable contribution to traditional protein classifications.

Availability: http://www.ebi.ac.uk/clustr/

Contact: rolf.apweiler{at}ebi.ac.uk

Received on April 5, 2005; revised on June 14, 2005; accepted on June 14, 2005

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 BioinformaticsHome page
M. Michaut, S. Kerrien, L. Montecchi-Palazzi, F. Chauvat, C. Cassier-Chauvat, J.-C. Aude, P. Legrain, and H. Hermjakob
InteroPORC: automated inference of highly conserved protein interaction networks
Bioinformatics, July 15, 2008; 24(14): 1625 - 1631.
[Abstract] [PDF]

Home page
 BioinformaticsHome page
Y. Loewenstein, E. Portugaly, M. Fromer, and M. Linial
Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space
Bioinformatics, July 1, 2008; 24(13): i41 - i49.
[Abstract] [PDF]

Home page
 BioinformaticsHome page
B. E. Suzek, H. Huang, P. McGarvey, R. Mazumder, and C. H. Wu
UniRef: comprehensive and non-redundant UniProt reference clusters
Bioinformatics, May 15, 2007; 23(10): 1282 - 1288.
[Abstract] [Full Text] [PDF]

Home page
 Nucleic Acids ResHome page
N. J. Mulder, R. Apweiler, T. K. Attwood, A. Bairoch, A. Bateman, D. Binns, P. Bork, V. Buillard, L. Cerutti, R. Copley, et al.
New developments in the InterPro database
Nucleic Acids Res., January 12, 2007; 35(suppl_1): D224 - D228.
[Abstract] [Full Text] [PDF]

Home page
 Nucleic Acids ResHome page
T. Rattei, R. Arnold, P. Tischler, D. Lindner, V. Stumpflen, and H. W. Mewes
SIMAP: the similarity matrix of proteins
Nucleic Acids Res., January 1, 2006; 34(suppl_1): D252 - D256.
[Abstract] [Full Text] [PDF]


Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.